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Recommending Contents Based on Zhihu Q&A Community: Case Study of Logistics Topics |
He Yue, Feng Yue, Zhao Shupeng(), Ma Yufeng |
Business School, Sichuan University, Chengdu 610064, China |
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Abstract [Objective] This research analyzes the social behaviors of Zhihu (https://www.zhihu.com/) users, aiming to recommend relevant contents more effectively. [Methods] First, we proposed a content recommendation method based on association rules-LDA topic model. Then, we constructed a network of shared sub-topics for specific topics and extracted keywords of the sub-topics with the LDA model. Finally, we pushed contents of the relevant topics for the users. [Results] Our study found that many sub-topics with high degrees of cooccurrence under the topic of logistics, and their confidence levels were above 65%. [Limitations] More comprehensive data is needed in future studies.[Conclusions] The association rule-LDA model provides new directions for content recommendation.
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Received: 18 January 2018
Published: 25 October 2018
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